We live in the era of data: it is mass produced, it is quite easy to access and can be processed extremely fast. We should not lose the focus in this accelerated process from the purpose of the KPIs created from the data. Are we sure the KPIs and statistical measures looked practical 20-30 years ago are still go best?

Let’s have a look at the most popular and the trickiest statistical indicator, the simple average. The general view is that the average salary describes well the typical salary of a population. However the average is usually much higher than the more representative median. To understand what median means, think about a group of kids lined up based on their height. The height of the kid standing in the middle of the line is the median height of the group. Based on this the median salary in Hungary is the point at which half of the population earns more, and half of it earns less. Based on EUROSTAT data the median salary in Hungary in 2016 was 4772 EUR while the average salary was 5397 EUR. We would like to believe that the “average costumer” manages 5397 EUR, but unfortunately the 4772 EUR is much more accurate. Median tends to be lower, but tends to be truer than average.

We experience much bigger differences in the enterprise datasets, especially if the calculation is based on fewer clients or wider range. For example: we have 100 businesses in a database with a typical income around 1 million USD. In this case, if a company with an income of 100 million USD enters the dataset the average goes to double just because of that one firm! Still, the group is better represented by the value of the median which is still 1 million USD.

Another example is how to decrease the average call time of 10 minutes of a call center employer who makes 100 calls with 30 seconds each. A crafty analyst who knows about the nature of average would suggest to skip the only 1-hour-long call and so, the target is reached! But did the performance of the employer changed? No, because the median call time remained the same.

It is advised to remember how fragile the average can be to even only one data quality fault or to any extremity.

The average has become more popular against the median because it is easy to calculate from the values and the number of records, therefore it was easy to handle way before the era of computers and databases.

But we live in the era of databases when it is worth to use the more accurate median in the business reports to describe our clients.

Hiflylabs creates business value from data. The core of the team has been working together for 15 years, currently with more than 50 passionate employees.